Coalbed Methane(CBM) production enhancement for single wells is a big problem to CBM industrialization. Low production is due to insufficient gas generation by thermogenic. Luckily, Biogenic gas was found in many areas and its supply is assumed to improve coalbed methane production. Therefore, microbial simulation experiment will demonstrate the effectiveness of the assumption. From microbial simulation experiment on different coal ranks, it is found that microbes can use coals to produce biogas under laboratory conditions. With different temperatures for different experiments, it turns out that the gas production at 35 ℃ is greater than that at 15℃,indicating that 35℃ is more suitable for microbes to produce gas. According to quantitative experiments, adding exogenous nutrients or exogenous bacteria can improve CBM production. Moreover, the production enhancement ratio can reach up to 115% under the condition of adding exogenous bacterial species, while the ratio for adding nutrients can be up to 144%.

This study examined ways to prevent failures in the front-end of forklifts by addressing the center of gravity of heavy objects carried by forklifts, predicting the remaining useful lifetime (RUL), and the fault diagnosis based on alarm rules. In the research process, acceleration signals were acquired from the outer beam of the front-end of the forklift. A one-second window was applied to extract the time-domain statistical features, which were then set as variables. An exponentially weighted moving average was used to smooth the noise in the feature data set. The AWGN and LSTM autoencoders were used for data augmentation. Based on them, random forest and lightGBM models were used to develop classification models for the weight center of heavy objects carried by a forklift. In addition, contextual diagnosis performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models showed that random forest achieved an accuracy of 0.9563 and lightGBM achieved an accuracy of 0.9566. In addition, the acceleration data were collected through experiments to predict forklift failure and RUL because of the repeated forklift use when the center of heavy objects carried by the forklift was skewed to the right. The time-domain statistical features of the acceleration signals were extracted and set as variables by applying a 20-second window. Subsequently, logistic regression and random forest models were used to classify the failure stages of the forklifts. The f1-score (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. In addition, the random forest probabilities for each stage were combined and averaged to generate a degradation curve and derive the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and the RUL prediction model was developed to predict the failure point. In addition, the SHAP algorithm was used to identify the significant features in classifying the stage. The alarm rule-based fault diagnosis was performed using the threshold of the normal stage distribution of the significant features.

The military taxonomy of class indices for chemical and biological warfare agents considers halogenated tear agents (CS) to be incapacitating causing local pain and discomfort with associated reflexe. The removal yield of the organic substances present in the water depends on the environmental conditions, on the chemical composition of the water and on the chemical substance dissolved in the water, which constitutes the substrate of the metabolic activities of the microorganisms that use these substances in the biochemical reactions of the cellular enzyme complexes. The two -CN groups in the malonic nitrile molecule are strongly electron-attracting and activate the CH2 group to which they are linked, and because of this, malononitrile has the role of a methylene component in condensation reactions with aldehydes or ketones. The results obtained after analyzing the degree of hydrolysis in the samples that contained the biological suspension indicated that no CBM metabolites were detected in any biological sample, regardless of the test concentration, analyzed 24 h after incubation. In the parallel samples, the aqueous solutions of CBM without biological treatment had metabolites in the samples., at 30 minutes the degree of hydrolysis is 0.21%, at 60 minutes, the degree of hydrolysis is 5.42%, after 90 minutes, the degree of hydrolysis is 6.19%, and after 24 hours. the degree of hydrolysis is 23.41%. Chemical substances in contact with microorganisms are used by them in the biochemical processes in which they are involved, the retention time and biodegradation capacity have led to an increased effect of the metabolism of toxic substances, in metabolic reactions, organic substances are the source of carbon and energy for biochemical processes. The tests performed indicate that the suspension of Chlorella sp. consumed the entire amount of CBM and metabolites from the analyzed samples The tests prove that the biological material can be used for the decontamination of the affected areas.

In the absence of a treaty protocol or verification regime, the Biological and Toxin Weapons Convention (BWC) instituted Confidence-Building Measures (CBMs) as a mechanism to increase confidence in compliance, by enhancing transparency and mitigating ambiguities regarding states parties’ biological activities. While a promising tool to support treaty compliance, low participation, concerns regarding the completeness and accuracy of CBM submissions, a dearth of analysis, and restricted access to many submissions have limited CBMs’ value. Through interviews with 53 international experts—38 from BWC delegations and 15 independent experts—we identified concrete opportunities to increase CBMs’ value while mitigating the burden on states parties. This study supports states parties’ efforts in the BWC Working Group on the Strengthening of the Convention, as part of a series of research on BWC assurance that aims to characterize challenges around BWC verification and increase certainty in BWC compliance.